{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy\n",
    "import gensim\n",
    "import numpy as np\n",
    "from jieba import analyse\n",
    "from gensim.models import Word2Vec\n",
    "from gensim.models.word2vec import LineSentence"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "与'文化'相似度最高的10个词: \n",
      "[('地理', 0.691535472869873), ('娱乐', 0.6754871606826782), ('民间', 0.6676011085510254), ('现代', 0.6536717414855957), ('社会学', 0.6414526104927063), ('交流', 0.640458881855011), ('艺术', 0.6403443813323975), ('哲学', 0.6373332142829895), ('地域', 0.6313769221305847), ('民族', 0.6262353658676147)]\n"
     ]
    }
   ],
   "source": [
    "def train_word2vec():\n",
    "    cor_data=open('TrainData.txt','r',encoding='utf-8')\n",
    "    model=Word2Vec(LineSentence(cor_data),sg=0,vector_size=200,window=5,min_count=5,workers=9)\n",
    "    model.save('model_word2vec')\n",
    "    print(\"与'文化'相似度最高的10个词: \")\n",
    "    print(model.wv.most_similar('文化',topn=10))\n",
    "train_word2vec()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def keyword(data):\n",
    "    tfidf=analyse.extract_tags\n",
    "    keywords=tfidf(data)\n",
    "    return keywords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_keywords(docpath,savepath):\n",
    "    with open(docpath, 'r', encoding='utf-8') as docf, open(savepath,'w') as outf:\n",
    "        for data in docf:\n",
    "            data=data[:len(data)-1]\n",
    "            keywords=keyword(data)\n",
    "            for word in keywords:\n",
    "                outf.write(word + ' ')\n",
    "            outf.write('\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_pos(string,char):\n",
    "    space_pos=[]\n",
    "    try:\n",
    "        space_pos=list(((pos) for pos, val in enumerate(string)if(val == char)))\n",
    "    except:\n",
    "        pass\n",
    "    return space_pos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_vector(file_name,model):\n",
    "    with open(file_name,'r') as f:\n",
    "        wordvec_size=200\n",
    "        word_vector=numpy.zeros(wordvec_size)\n",
    "        for data in f:\n",
    "            space_pos=get_pos(data,' ')\n",
    "            first_word=data[0:space_pos[0]]\n",
    "            if model.wv.__contains__(first_word):\n",
    "                word_vector=word_vector + model.wv[first_word]\n",
    "            for i in range(len(space_pos)-1):\n",
    "                word=data[space_pos[i]:space_pos[i+1]]\n",
    "                if model.wv.__contains__(word):\n",
    "                    word_vector=word_vector+model.wv[word]\n",
    "        return word_vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "def similarity(vector1,vector2):\n",
    "    vector1_abs=np.sqrt(vector1.dot(vector1))\n",
    "    vector2_abs=np.sqrt(vector2.dot(vector2))\n",
    "    if vector2_abs != 0 and vector1_abs != 0:\n",
    "        similarity = (vector1.dot(vector2))/(vector1_abs * vector2_abs)\n",
    "    else:\n",
    "        similarity = 0\n",
    "    return similarity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "def main():\n",
    "    model=gensim.models.Word2Vec.load('model_word2vec')\n",
    "    new1='new1.txt'\n",
    "    new2='new2.txt'\n",
    "    new1_keywords='new1_keywords.txt' \n",
    "    new2_keywords='new2_keywords.txt' \n",
    "    get_keywords(new1,new1_keywords)\n",
    "    get_keywords(new2,new2_keywords)\n",
    "    new1_vector=get_vector(new1_keywords,model)\n",
    "    print('文本new1的部分向量: \\n',new1_vector[:20])\n",
    "    new2_vector=get_vector(new2_keywords,model)\n",
    "    print('文本new2的部分向量: \\n',new2_vector[:20])\n",
    "    print('文本new1和文本new2的相似度：',similarity(new1_vector,new2_vector))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "文本new1的部分向量: \n",
      " [ 0.65738727  0.05049371 -0.21316291  0.93043519  0.64929274 -0.10091115\n",
      "  0.73503313  0.66626944 -0.54242894 -0.44239902 -0.14663331  0.6641066\n",
      "  0.07715336  0.84071852 -0.3643375  -0.96663813  0.43977691  0.19887762\n",
      "  0.89165095 -0.56509349]\n",
      "文本new2的部分向量: \n",
      " [ 1.97628432  0.58556328 -0.57316722  0.24257871  1.82824965  0.3278496\n",
      "  1.20554101 -0.81447649 -1.24729772 -0.49260807 -1.05693738  2.00430819\n",
      " -0.16051888  1.55268782 -0.55167768 -1.38238615  1.18932232  0.58020576\n",
      "  0.65695781  0.58886063]\n",
      "文本new1和文本new2的相似度： 0.704383305440255\n"
     ]
    }
   ],
   "source": [
    "if __name__ == \"__main__\":\n",
    "    main()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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